Fatigue Assessment from Facial Videos using Deep Neural Networks and Engineered Features Informed by Domain Knowledge.

Fatigue impairs cognitive and motor function, potentially leading to mishaps in high-pressure occupations such as aviation and emergency medical services. The current approach is primarily based on self-assessment, which is subjective and error-prone. An objective method is needed to detect severe and likely dangerous levels of fatigue quickly and accurately. Here, we present a quantitative evaluation tool that uses less than two minutes of facial video, captured using an iPad, to assess fatigue vs. alertness. The tool is fast, easy to use, and scalable since it uses cameras readily available on consumer-electronic devices. We compared the classification performance between a Long Short-Term Memory (LSTM) deep neural network and a Random Forest (RF) classifier applied to engineered features informed by domain knowledge. The preliminary results on an 11-subject dataset show that RF outperforms LSTM, with added interpretability on the features used. For the RF classifiers, the average areas under the receiver operating characteristic curve, based on the 11-fold and individualized 11-fold cross validations, are 0.72 ± 0.16 and 0.8 ± 0.12, respectively. Equal error rates are 0.34 and 0.26, respectively. This study presents a promising approach for rapid fatigue detection. Additional data will be collected to assess the generalizability across populations.

Diagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection.

Many early warning algorithms are downstream of clinical evaluation and diagnostic testing, which means that they may not be useful when clinicians fail to suspect illness and fail to order appropriate tests. Depending on how such algorithms handle missing data, they could even indicate "low risk" simply because the testing data were never ordered. We considered predictive methodologies to identify sepsis at triage, before diagnostic tests are ordered, in a busy Emergency Department (ED). One algorithm used "bland clinical data" (data available at triage for nearly every patient). The second algorithm added three yes/no questions to be answered after the triage interview. Retrospectively, we studied adult patients from a single ED between 2014-16, separated into training (70%) and testing (30%) cohorts, and a final validation cohort of patients from four EDs between 2016-2018. Sepsis was defined per the Rhee criteria. Investigational predictors were demographics and triage vital signs (downloaded from the hospital EMR); past medical history; and the auxiliary queries (answered by chart reviewers who were blinded to all data except the triage note and initial HPI). We developed L2-regularized logistic regression models using a greedy forward feature selection. There were 1164, 499, and 784 patients in the training, testing, and validation cohorts, respectively. The bland clinical data model yielded ROC AUC's 0.78 (0.76-0.81) and 0.77 (0.73-0.81), for training and testing, respectively, and ranged from 0.74-0.79 in four hospital validation. The second model which included auxiliary queries yielded 0.84 (0.82-0.87) and 0.83 (0.79-0.86), and ranged from 0.78-0.83 in four hospital validation. The first algorithm did not require clinician input but yielded middling performance. The second showed a trend towards superior performance, though required additional user effort. These methods are alternatives to predictive algorithms downstream of clinical evaluation and diagnostic testing. For hospital early warning algorithms, consideration should be given to bias and usability of various methods.

Low-Order Mechanistic Models for Volumetric and Temporal Capnography: Development, Validation, and Application.

OBJECTIVE: Develop low-order mechanistic models accounting quantitatively for, and identifiable from, the capnogram - the CO 2 concentration in exhaled breath, recorded over time (Tcap) or exhaled volume (Vcap). METHODS: The airflow model's single "alveolar" compartment has compliance and inertance, and feeds a resistive unperfused airway comprising a laminar-flow region followed by a turbulent-mixing region. The gas-mixing model tracks mixing-region CO 2 concentration, fitted breath-by-breath to the measured capnogram, yielding estimates of model parameters that characterize the capnogram. RESULTS: For the 17 examined records (310 breaths) of airflow, airway pressure and Tcap from ventilated adult patients, the models fit closely (mean rmse 1% of end-tidal CO 2 concentration on Vcap; 1.7% on Tcap). The associated parameters (4 for Vcap, 5 for Tcap) for each exhalation, and airflow parameters for the corresponding forced inhalation, are robustly estimated, and consonant with literature values. The models also allow, using Tcap alone, estimation of the entire exhaled airflow waveform to within a scaling. This suggests new Tcap-based tests, analogous to spirometry but with normal breathing, for discriminating chronic obstructive pulmonary disease (COPD) from congestive heart failure (CHF). A version trained on 15 exhalations from each of 24 COPD/24 CHF Tcap records from one hospital, then tested 100 times with 15 random exhalations from each of 27 COPD/31 CHF Tcap records at another, gave mean accuracy 80.6% (stdev 2.1%). Another version, tested on 29 COPD/32 CHF, yielded AUROC 0.84. CONCLUSION: Our mechanistic models closely fit Tcap and Vcap measurements, and yield subject-specific parameter estimates. SIGNIFICANCE: This can inform cardiorespiratory care.

Deconvolution-Based Partial Volume Correction for Volumetric Blood Flow Measurement.

Ultrasound-based blood flow (BF) monitoring is vital in the diagnosis and treatment of a variety of cardiovascular and neurologic conditions. Finite spatial resolution of clinical color flow (CF) systems, however, has hampered measurement of vessel cross Section areas. We propose a resolution enhancement technique that allows reliable determination of BF in small vessels. We leverage sparsity in the spatial distribution of the frequency spectrum of routinely collected CF data to blindly determine the point spread function (PSF) of the imaging system in a robust manner. The CF data are then deconvolved with the PSF, and the volumetric flow is computed using the resulting velocity profiles. Data were collected from phantom blood vessels with diameters between 2 and 6 mm using a clinical ultrasound system at 2 MHz insonation frequency. The proposed method yielded a flow estimation bias of 0 mL/min, standard deviation of error (SDE) of 22 mL/min, and a root-mean-square error (RMSE) of 22 mL/min over a 150 mL/min range of mean flows. Recordings were also obtained in low signal-to-noise ratio (SNR) conditions using a skull mimicking element, resulting in an estimation bias of -13 mL/min, SDE of 23 mL/min, and an RMSE of 26 mL/min. The effect of insonation frequency was also investigated by obtaining recordings at 4.3 MHz, yielding an estimation bias of -16 mL/min, SDE of 16 mL/min, and an RMSE of 22 mL/min. The results indicate that our technique can lead to clinically acceptable flow measurements across a range of vessel diameters in high and low SNR regimes.

Modeling of Usual Care: Vasopressor Initiation for Sepsis With Hypotension.

Usual care regarding vasopressor initiation is ill-defined. We aimed to develop a quantitative "dynamic practice" model for usual care in the emergency department (ED) regarding the timing of vasopressor initiation in sepsis. In a retrospective study of 589 septic patients with hypotension in an urban tertiary care center ED, we developed a multi-variable model that distinguishes between patients who did and did not subsequently receive sustained (>24 h) vasopressor therapy. Candidate predictors were vital signs, intravenous fluid (IVF) volumes, laboratory measurements, and elapsed time from triage computed at timepoints leading up to the final decision timepoint of either vasopressor initiation or ED hypotension resolution without vasopressors. A model with six independently significant covariates (respiratory rate, Glasgow Coma Scale score, SBP, SpO2, administered IVF, and elapsed time) achieved a C-statistic of 0.78 in a held-out test set at the final decision timepoint, demonstrating the ability to reliably model usual care for vasopressor initiation for hypotensive septic patients. The included variables measured depth of hypotension, extent of disease severity and organ dysfunction. At an operating point of 90% specificity, the model identified a minority of patients (39%) more than an hour before actual vasopressor initiation, during which time a median of 2,250 (IQR 1,200-3,300) mL of IVF was administered. This single-center analysis shows the feasibility of a quantitative, objective tool for describing usual care. Dynamic practice models may help assess when management was atypical; such tools may also be useful for designing and interpreting clinical trials.

App-Based Saccade Latency and Directional Error Determination Across the Adult Age Spectrum.

OBJECTIVE: We aid in neurocognitive monitoring outside the hospital environment by enabling app-based measurements of visual reaction time (saccade latency) and directional error rate in a cohort of subjects spanning the adult age spectrum. METHODS: We developed an iOS app to record subjects with the frontal camera during pro- and anti-saccade tasks. We further developed automated algorithms for measuring saccade latency and directional error rate that take into account the possibility that it might not always be possible to determine the eye movement from app-based recordings. RESULTS: To measure saccade latency on a tablet, we ensured that the absolute timing error between on-screen task presentation and the camera recording is within 5 ms. We collected over 235,000 eye movements in 80 subjects ranging in age from 20 to 92 years, with 96% of recorded eye movements either declared good or directional errors. Our error detection code achieved a sensitivity of 0.97 and a specificity of 0.97. Confirming prior reports, we observed a positive correlation between saccade latency and age while the relationship between directional error rate and age was not significant. Finally, we observed significant intra- and inter-subject variations in saccade latency and directional error rate distributions, which highlights the importance of individualized tracking of these visual digital biomarkers. CONCLUSION AND SIGNIFICANCE: Our system and algorithms allow ubiquitous tracking of saccade latency and directional error rate, which opens up the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.

Cerebral Emboli Monitoring Using Transcranial Doppler Ultrasonography in Adults and Children: A Review of the Current Technology and Clinical Applications in the Perioperative and Intensive Care Setting.

Transcranial Doppler (TCD) ultrasonography is the only noninvasive bedside technology for the detection and monitoring of cerebral embolism. TCD may identify patients at risk of acute and chronic neurologic injury from gaseous or solid emboli. Importantly, a window of opportunity for intervention-to eliminate the source of the emboli and thereby prevent subsequent development of a clinical or subclinical stroke-may be identified using TCD. In this review, we discuss the application of TCD sonography in the perioperative and intensive care setting in adults and children known to be at increased risk of cerebral embolism. The major challenge for evaluation of emboli, especially in children, is the need to establish the ground truth and define true emboli identified by TCD. This requires the development and validation of a predictive TCD emboli monitoring technique so that appropriately designed clinical studies intended to identify specific modifiable factors and develop potential strategies to reduce pathologic cerebral embolic burden can be performed.

Long-duration spaceflight alters estimated intracranial pressure and cerebral blood velocity.

KEY POINTS: During long-duration spaceflights, some astronauts develop structural ocular changes including optic disc oedema that resemble signs of intracranial hypertension. In the present study, intracranial pressure was estimated non-invasively (nICP) using a model-based analysis of cerebral blood velocity and arterial blood pressure waveforms in 11 astronauts before and after long-duration spaceflights. Our results show that group-averaged estimates of nICP decreased significantly in nine astronauts without optic disc oedema, suggesting that the cephalad fluid shift during long-duration spaceflight rarely increased postflight intracranial pressure. The results of the two astronauts with optic disc oedema suggest that both increases and decreases in nICP are observed post-flight in astronauts with ocular alterations, arguing against a primary causal relationship between elevated ICP and spaceflight associated optical changes. Cerebral blood velocity increased independently of nICP and spaceflight-associated ocular alterations. This increase may be caused by the reduced haemoglobin concentration after long-duration spaceflight. ABSTRACT: Persistently elevated intracranial pressure (ICP) above upright values is a suspected cause of optic disc oedema in astronauts. However, no systematic studies have evaluated changes in ICP from preflight. Therefore, ICP was estimated non-invasively before and after spaceflight to test whether ICP would increase after long-duration spaceflight. Cerebral blood velocity in the middle cerebral artery (MCAv) was obtained by transcranial Doppler sonography and arterial pressure in the radial artery was obtained by tonometry, in the supine and sitting positions before and after 4-12 months of spaceflight in 11 astronauts (10 males and 1 female, 46 ± 7 years old at launch). Non-invasive ICP (nICP) was computed using a validated model-based estimation method. Mean MCAv increased significantly after spaceflight (ANOVA, P = 0.007). Haemoglobin decreased significantly after spaceflight (14.6 ± 0.8 to 13.3 ± 0.7 g/dL, P < 0.001). A repeated measures correlation analysis indicated a negative correlation between haemoglobin and mean MCAv (r = -0.589, regression coefficient = -4.68). The nICP did not change significantly after spaceflight in the 11 astronauts. However, nICP decreased significantly by 15% in nine astronauts without optic disc oedema (P < 0.005). Only one astronaut increased nICP to relatively high levels after spaceflight. Contrary to our hypothesis, nICP did not increase after long-duration spaceflight in the vast majority (>90%) of astronauts, suggesting that the cephalad fluid shift during spaceflight does not systematically or consistently elevate postflight ICP in astronauts. Independently of nICP and ocular alterations, the present results of mean MCAv suggest that long-duration spaceflight may increase cerebral blood flow, possibly due to reduced haemoglobin concentration.

Distortion of the Intracranial Pressure Waveform by Extraventricular Drainage System.

OBJECTIVE: To investigate whether intracranial pressure (ICP) waveform measurements obtained from extraventricular drainage (EVD) systems are suitable for the calculation of intracranial elastance (ICE) or cerebrovascular pressure autoregulation (PAR) indices. METHODS: The transfer characteristic of an EVD system is investigated by its step and frequency responses with focus on the low frequency (LF) range from 0.02 to 0.065 Hz (important in PAR) and the location of the system's first resonance frequency (important for ICE). The effects of opening the distal end of the EVD for drainage of cerebrospinal fluid and the presence of trapped air bubbles are also investigated. RESULTS: The EVD system exhibits a first resonant frequency below 4 Hz, resulting in significant distortion of the measured ICP waveform. The frequency response in the LF range only remains flat when the EVD is closed. Opening the drain results in drops in magnitude and phase along the entire frequency range above DC. Air bubbles close to the EVD catheter tip affect the LF range while an air bubble close to the pressure transducer further decreases the first resonant frequency. Tests with actual ICP waveforms confirmed EVD-induced waveform distortions that can lead to erroneous ICE estimation. CONCLUSION: EVD-based ICP measurements distort the waveform morphology. PAR indices based on LF information are only valid if the EVD is closed. EVD-based ICE estimation is to be avoided. SIGNIFICANCE: ICP waveform analyses to derive information about ICE and PAR should be critically questioned if only EVD derived ICP signals are at hand.

Validation of a 'Usual Care' Model for Vasopressor Initiation in a Cohort of Emergency Department Patients with Sepsis.

Usual care regarding vasopressor (VP) initiation is ill-defined. We aimed to further validate a quantitative model for usual care in the Emergency Department (ED) regarding the timing of VP initiation in sepsis. We retrospectively studied a cohort of adult critically-ill ED patients who also received antibiotics in the ED. We applied a multivariable model previously developed from another patient cohort which distinguishes between time points at which patients were or were not subsequently started on a continuous VP infusion. The model has six independently significant predictors (respiratory rate, Glasgow Coma Scale score, systolic blood pressure, SpO2, administered intravenous fluids, and elapsed time). The outcome was initiation of VP infusion, either within the ED or within 6 hours after leaving the ED. We applied the model to all time points, beginning when all model input parameters were first available for a given patient, and ending when either VP were first started, or the patient left the ED. Out of 55,963 adult ED patients during the two-year study interval, we identified 1,629 who met our inclusion criteria. The area under the receiver operating characteristic curve was 0.81 for all patients, and 0.72 for the subset with at least one hypotensive blood pressure measurement. At a model threshold with sensitivity and specificity 0.74 and 0.74, respectively, the median advance detection time was 170.5 minutes (IQR 53 - 363).

Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos.

OBJECTIVE: Novel applications of transcranial Doppler (TCD) ultrasonography, such as the assessment of cerebral vessel narrowing/occlusion or the non-invasive estimation of intracranial pressure (ICP), require high-quality maximal flow velocity waveforms. However, due to the low signal-to-noise ratio of TCD spectrograms, measuring the maximal flow velocity is challenging. In this work, we propose a calibration-free algorithm for estimating maximal flow velocities from TCD spectrograms and present a pertaining beat-by-beat signal quality index. METHODS: Our algorithm performs multiple binary segmentations of the TCD spectrogram and then extracts the pertaining envelopes (maximal flow velocity waveforms) via an edge-following step that incorporates physiological constraints. The candidate maximal flow velocity waveform with the highest signal quality index is finally selected. RESULTS: We evaluated the algorithm on 32 TCD recordings from the middle cerebral and internal carotid arteries in 6 healthy and 12 neurocritical care patients. Compared to manual spectrogram tracings, we obtained a relative error of -1.5%, when considering the whole waveform, and a relative error of -3.3% for the peak systolic velocity. CONCLUSION: The feedback loop between the signal quality assessment and the binary segmentation yields a robust algorithm for maximal flow velocity estimation. Clinical Impact: The algorithm has already been used in our ICP estimation pipeline. By making the code and the data publicly available, we hope that the algorithm will be a useful building block for the development of novel TCD applications that require high-quality flow velocity waveforms.

Antibiotic Delays and Feasibility of a 1-Hour-From-Triage Antibiotic Requirement: Analysis of an Emergency Department Sepsis Quality Improvement Database.

STUDY OBJECTIVE: We identify factors associated with delayed emergency department (ED) antibiotics and determine feasibility of a 1-hour-from-triage antibiotic requirement in sepsis. METHODS: We studied all ED adult septic patients in accordance with Centers for Medicare & Medicaid Services Severe Sepsis and Septic Shock National Quality Measures in 2 consecutive 12-month intervals. During the second interval, a quality improvement intervention was conducted: a sepsis screening protocol plus case-specific feedback to clinicians. Data were abstracted retrospectively through electronic query and chart review. Primary outcomes were antibiotic delay greater than 3 hours from documented onset of hypoperfusion (per Centers for Medicare & Medicaid Services Severe Sepsis and Septic Shock National Quality Measures) and antibiotic delay greater than 1 hour from triage (per 2018 Surviving Sepsis Campaign recommendations). RESULTS: We identified 297 and 357 septic patients before and during the quality improvement intervention, respectively. Before and during quality improvement intervention, antibiotic delay in accordance with Centers for Medicare & Medicaid Services measures occurred in 30% and 21% of cases (-9% [95% confidence interval -16% to -2%]); and in accordance with 2018 Surviving Sepsis Campaign recommendations, 85% and 71% (-14% [95% confidence interval -20% to -8%]). Four factors were independently associated with both definitions of antibiotic delay: vague (ie, nonexplicitly infectious) presenting symptoms, triage location to nonacute areas, care before the quality improvement intervention, and lower Sequential [Sepsis-related] Organ Failure Assessment scores. Most patients did not receive antibiotics within 1 hour of triage, with the exception of a small subset post-quality improvement intervention who presented with explicit infectious symptoms and triage hypotension. CONCLUSION: The quality improvement intervention significantly reduced antibiotic delays, yet most septic patients did not receive antibiotics within 1 hour of triage. Compliance with the 2018 Surviving Sepsis Campaign would require a wholesale alteration in the management of ED patients with either vague symptoms or absence of triage hypotension.

Pseudo-Bayesian Model-Based Noninvasive Intracranial Pressure Estimation and Tracking.

OBJECTIVE: A noninvasive intracranial pressure (ICP) estimation method is proposed that incorporates a model-based approach within a probabilistic framework to mitigate the effects of data and modeling uncertainties. METHODS: A first-order model of the cerebral vasculature relates measured arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) to ICP. The model is driven by the ABP waveform and is solved for a range of mean ICP values to predict the CBFV waveform. The resulting errors between measured and predicted CBFV are transformed into likelihoods for each candidate ICP in two steps. First, a baseline ICP estimate is established over five data windows of 20 beats by combining the likelihoods with a prior distribution of the ICP to yield an a posteriori distribution whose median is taken as the baseline ICP estimate. A single-state model of cerebral autoregulatory dynamics is then employed in subsequent data windows to track changes in the baseline by combining ICP estimates obtained with a uniform prior belief and model-predicted ICP. For each data window, the estimated model parameters are also used to determine the ICP pulse pressure. RESULTS: On a dataset of thirteen pediatric patients with a variety of pathological conditions requiring invasive ICP monitoring, the method yielded for mean ICP estimation a bias (mean error) of 0.6 mmHg and a root-mean-squared error of 3.7 mmHg. CONCLUSION: These performance characteristics are well within the acceptable range for clinical decision making. SIGNIFICANCE: The method proposed here constitutes a significant step towards robust, continuous, patient-specific noninvasive ICP determination.

Model-Based Separation, Detection, and Classification of Eye Movements.

OBJECTIVE: We present a physiologically motivated eye movement analysis framework for model-based separation, detection, and classification (MBSDC) of eye movements. By estimating kinematic and neural controller signals for saccades, smooth pursuit, and fixational eye movements in a mechanistic model of the oculomotor system we are able to separate and analyze these eye movements independently. METHODS: We extended an established oculomotor model for horizontal eye movements by neural controller signals and by a blink artifact model. To estimate kinematic (position, velocity, acceleration, forces) and neural controller signals from eye position data, we employ Kalman smoothing and sparse input estimation techniques. The estimated signals are used for detecting saccade start and end points, and for classifying the recording into saccades, smooth pursuit, fixations, post-saccadic oscillations, and blinks. RESULTS: On simulated data, the reconstruction error of the velocity profiles is about half the error value obtained by the commonly employed approach of filtering and numerical differentiation. In experiments with smooth pursuit data from human subjects, we observe an accurate signal separation. In addition, in neural recordings from non-human primates, the estimated neural controller signals match the real recordings strikingly well. SIGNIFICANCE: The MBSDC framework enables the analysis of multi-type eye movement recordings and provides a physiologically motivated approach to study motor commands and might aid the discovery of new digital biomarkers. CONCLUSION: The proposed framework provides a model-based approach for a wide variety of eye movement analysis tasks.

Measuring Saccade Latency Using Smartphone Cameras.

OBJECTIVE: Accurate quantification of neurodegenerative disease progression is an ongoing challenge that complicates efforts to understand and treat these conditions. Clinical studies have shown that eye movement features may serve as objective biomarkers to support diagnosis and tracking of disease progression. Here, we demonstrate that saccade latency-an eye movement measure of reaction time-can be measured robustly outside of the clinical environment with a smartphone camera. METHODS: To enable tracking of saccade latency in large cohorts of patients and control subjects, we combined a deep convolutional neural network for gaze estimation with a model-based approach for saccade onset determination that provides automated signal-quality quantification and artifact rejection. RESULTS: Simultaneous recordings with a smartphone and a high-speed camera resulted in negligible differences in saccade latency distributions. Furthermore, we demonstrated that the constraint of chinrest support can be removed when recording healthy subjects. Repeat smartphone-based measurements of saccade latency in 11 self-reported healthy subjects resulted in an intraclass correlation coefficient of 0.76, showing our approach has good to excellent test-retest reliability. Additionally, we conducted more than 19 000 saccade latency measurements in 29 self-reported healthy subjects and observed significant intra- and inter-subject variability, which highlights the importance of individualized tracking. Lastly, we showed that with around 65 measurements we can estimate mean saccade latency to within less-than-10-ms precision, which takes within 4 min with our setup. CONCLUSION AND SIGNIFICANCE: By enabling repeat measurements of saccade latency and its distribution in individual subjects, our framework opens the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.

A Time-Frequency Approach for Cerebral Embolic Load Monitoring.

OBJECTIVE: To enable reliable cerebral embolic load monitoring from high-intensity transient signals (HITS) recorded with single-channel transcranial Doppler (TCD) ultrasound. METHODS: We propose a HITS detection and characterization method using a weighted-frequency Fourier linear combiner that estimates baseline Doppler signal power. An adaptive threshold is determined by examining the Doppler signal power variance about the baseline estimate, and HITS are extracted if their Doppler power exceeds this threshold. As signatures from multiple emboli may be superimposed, we analyze the detected HITS in the time-frequency (TF) domain to segment the signals into individual emboli. A logistic regression classification approach is employed to classify HITS into emboli or artifacts. Data were collected using a commercial TCD device with emboli-detection capabilities from 12 children undergoing mechanical circulatory support or cardiac catheterization. A subset of 696 HITS were reviewed, annotated, and split into training and testing sets for developing and evaluating the HITS classification algorithm. RESULTS: The classifier yielded 98% and 96% sensitivity for 100% specificity on training and testing data, respectively. The TF approach decomposed 38% of candidate embolic signals into two or more embolic events that ultimately account for 69% of the overall embolic counts. Our processing pipeline resulted in highly accurate emboli identification and produced emboli counts that were lower (by a median of 64%) compared to the commercial ultrasound system's estimates. SIGNIFICANCE: Using only single-channel, single-frequency Doppler ultrasound, the proposed method enables sensitive detection and segmentation of embolic signatures. Our approach paves the way toward accurate real-time cerebral emboli monitoring.

A Spectral Approach to Model-Based Noninvasive Intracranial Pressure Estimation.

BACKGROUND: Intracranial pressure (ICP) normally ranges from 5 to 15 mmHg. Elevation in ICP is an important clinical indicator of neurological injury, and ICP is therefore monitored routinely in several neurological conditions to guide diagnosis and treatment decisions. Current measurement modalities for ICP monitoring are highly invasive, largely limiting the measurement to critically ill patients. An accurate noninvasive method to estimate ICP would dramatically expand the pool of patients that could benefit from this cranial vital sign. METHODS: This article presents a spectral approach to model-based ICP estimation from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) measurements. The model captures the relationship between the ABP, CBFV, and ICP waveforms and utilizes a second-order model of the cerebral vasculature to estimate ICP. RESULTS: The estimation approach was validated on two separate clinical datasets, one recorded from thirteen pediatric patients with a total duration of around seven hours, and the other recorded from five adult patients, one hour and 48 minutes in total duration. The algorithm was shown to have an accuracy (mean error) of 0.4 mmHg and -1.5 mmHg, and a precision (standard deviation of the error) of 5.1 mmHg and 4.3 mmHg, in estimating mean ICP (range of 1.3 mmHg to 24.8 mmHg) on the pediatric and adult data, respectively. These results are comparable to previous results and within the clinically relevant range. Additionally, the accuracy and precision in estimating the pulse pressure of ICP on a beat-by-beat basis were found to be 1.3 mmHg and 2.9 mmHg respectively. CONCLUSION: These contributions take a step towards realizing the goal of implementing a real-time noninvasive ICP estimation modality in a clinical setting, to enable accurate clinical-decision making while overcoming the drawbacks of the invasive ICP modalities.

Fully automated, real-time, calibration-free, continuous noninvasive estimation of intracranial pressure in children.

OBJECTIVE: In the search for a reliable, cooperation-independent, noninvasive alternative to invasive intracranial pressure (ICP) monitoring in children, various approaches have been proposed, but at the present time none are capable of providing fully automated, real-time, calibration-free, continuous and accurate ICP estimates. The authors investigated the feasibility and validity of simultaneously monitored arterial blood pressure (ABP) and middle cerebral artery (MCA) cerebral blood flow velocity (CBFV) waveforms to derive noninvasive ICP (nICP) estimates. METHODS: Invasive ICP and ABP recordings were collected from 12 pediatric and young adult patients (aged 2-25 years) undergoing such monitoring as part of routine clinical care. Additionally, simultaneous transcranial Doppler (TCD) ultrasonography-based MCA CBFV waveform measurements were performed at the bedside in dedicated data collection sessions. The ABP and MCA CBFV waveforms were analyzed in the context of a mathematical model, linking them to the cerebral vasculature's biophysical properties and ICP. The authors developed and automated a waveform preprocessing, signal-quality evaluation, and waveform-synchronization "pipeline" in order to test and objectively validate the algorithm's performance. To generate one nICP estimate, 60 beats of ABP and MCA CBFV waveform data were analyzed. Moving the 60-beat data window forward by one beat at a time (overlapping data windows) resulted in 39,480 ICP-to-nICP comparisons across a total of 44 data-collection sessions (studies). Moving the 60-beat data window forward by 60 beats at a time (nonoverlapping data windows) resulted in 722 paired ICP-to-nICP comparisons. RESULTS: Greater than 80% of all nICP estimates fell within ± 7 mm Hg of the reference measurement. Overall performance in the nonoverlapping data window approach gave a mean error (bias) of 1.0 mm Hg, standard deviation of the error (precision) of 5.1 mm Hg, and root-mean-square error of 5.2 mm Hg. The associated mean and median absolute errors were 4.2 mm Hg and 3.3 mm Hg, respectively. These results were contingent on ensuring adequate ABP and CBFV signal quality and required accurate hydrostatic pressure correction of the measured ABP waveform in relation to the elevation of the external auditory meatus. Notably, the procedure had no failed attempts at data collection, and all patients had adequate TCD data from at least one hemisphere. Last, an analysis of using study-by-study averaged nICP estimates to detect a measured ICP > 15 mm Hg resulted in an area under the receiver operating characteristic curve of 0.83, with a sensitivity of 71% and specificity of 86% for a detection threshold of nICP = 15 mm Hg. CONCLUSIONS: This nICP estimation algorithm, based on ABP and bedside TCD CBFV waveform measurements, performs in a manner comparable to invasive ICP monitoring. These findings open the possibility for rational, point-of-care treatment decisions in pediatric patients with suspected raised ICP undergoing intensive care.

Computational model of cardiovascular response to centrifugation and lower body cycling exercise.

Short-radius centrifugation combined with exercise has been suggested as a potential countermeasure against spaceflight deconditioning. Both the long-term and acute physiological responses to such a combination are incompletely understood. We developed and validated a computational model to study the acute cardiovascular response to centrifugation combined with lower body ergometer exercise. The model consisted of 21 compartments, including the upper body, renal, splanchnic, and leg circulation, as well as a four-chamber heart and pulmonary circulation. It also included the effects of gravity gradient and ergometer exercise. Centrifugation and exercise profiles were simulated and compared with experimental data gathered on 12 subjects exposed to a range of gravitational levels (1 and 1.4G measured at the feet) and workload intensities (25-100 W). The model was capable of reproducing cardiovascular changes (within ± 1 SD from the group-averaged behavior) due to both centrifugation and exercise, including dynamic responses during transitions between the different phases of the protocol. The model was then used to simulate the hemodynamic response of hypovolemic subjects (blood volume reduced by 5-15%) subjected to similar gravitational stress and exercise profiles, providing insights into the physiological responses of experimental conditions not tested before. Hypovolemic results are in agreement with the limited available data and the expected responses based on physiological principles, although additional experimental data are warranted to further validate our predictions, especially during the exercise phases. The model captures the cardiovascular response for a range of centrifugation and exercise profiles, and it shows promise in simulating additional conditions where data collection is difficult, expensive, or infeasible.NEW & NOTEWORTHY Artificial gravity combined with exercise is a potential countermeasure for spaceflight deconditioning, but the long-term and acute cardiovascular response to such gravitational stress is still largely unknown. We provide a novel mathematical model of the cardiovascular system that incorporates gravitational stress generated by centrifugation and lower body cycling exercise, and we validate it with experimental measurements from human subjects. Simulations of experimental conditions not used for model development corroborate the model's predictive capabilities.